Sunlight Inactivation of Viruses in Open-Water Unit Process Treatment Wetlands: Modeling Endogenous and Exogenous Inactivation Rates

Sunlight inactivation is an important mode of disinfection for viruses in surface waters. In constructed wetlands, for example, open-water cells can be used to promote sunlight disinfection and remove pathogenic viruses from wastewater. To aid in the design of these systems, we developed predictive models of virus attenuation that account for endogenous and exogenous sunlight-mediated inactivation mechanisms. Inactivation rate models were developed for two viruses, MS2 and poliovirus type 3; laboratory- and field-scale experiments were conducted to evaluate the models’ ability to estimate inactivation rates in a pilot-scale, open-water, unit-process wetland cell. Endogenous inactivation rates were modeled using either photoaction spectra or total, incident UVB irradiance. Exogenous inactivation rates were modeled on the basis of virus susceptibilities to singlet oxygen. Results from both laboratory- and field-scale experiments showed good agreement between measured and modeled inactivation rates. The modeling approach presented here can be applied to any sunlit surface water and utilizes easily measured inputs such as depth, solar irradiance, water matrix absorbance, singlet oxygen concentration, and the virus-specific apparent second-order rate constant with singlet oxygen (<i>k</i>₂). Interestingly, the MS2 <i>k</i>₂ in the open-water wetland was found to be significantly larger than <i>k</i>₂ observed in other waters in previous studies. Examples of how the model can be used to design and optimize natural treatment systems for virus inactivation are provided

A Search for leptophilic Z_(l) boson at future linear colliders

We study the possible dynamics associated with leptonic charge in future linear colliders. Leptophilic massive vector boson, Z_(l), have been investigated through the process e^(+)e^(-) -> mu^(+)mu^(-). We have shown that ILC and CLIC will give opportunity to observe Z_(l) with masses up to the center of mass energy if the corresponding coupling constant g_(l) exceeds 10^(-3).Comment: 12 pages, 10 figure

Active learning with RESSPECT: Resource allocation for extragalactic astronomical transients

The authors would like to thank David Kirkby and Connor Sheere for insightful discussions. This work is part of the Recommendation System for Spectroscopic Followup (RESSPECT) project, governed by an inter-collaboration agreement signed between the Cosmostatistics Initiative (COIN) and the LSST Dark Energy Science Collaboration (DESC). This research is supported in part by the HPI Research Center in Machine Learning and Data Science at UC Irvine. EEOI and SS acknowledge financial support from CNRS 2017 MOMENTUM grant under the project Active Learning for Large Scale Sky Surveys. SGG and AKM acknowledge support by FCT under Project CRISP PTDC/FIS-AST-31546/2017. This work was partly supported by the Hewlett Packard Enterprise Data Science Institute (HPE DSI) at the University of Houston. DOJ is supported by a Gordon and Betty Moore Foundation postdoctoral fellowship at the University of California, Santa Cruz. Support for this work was provided by NASA through the NASA Hubble Fellowship grant HF2-51462.001 awarded by the Space Telescope Science Institute, which is operated by the Association of Universities for Research in Astronomy, Inc., for NASA, under contract NAS5-26555. BQ is supported by the International Gemini Observatory, a program of NSF's NOIRLab, which is managed by the Association of Universities for Research in Astronomy (AURA) under a cooperative agreement with the National Science Foundation, on behalf of the Gemini partnership of Argentina, Brazil, Canada, Chile, the Republic of Korea, and the United States of America. AIM acknowledges support from the Max Planck Society and the Alexander von Humboldt Foundation in the framework of the Max Planck-Humboldt Research Award endowed by the Federal Ministry of Education and Research. L.G. was funded by the European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No. 839090. This work has been partially supported by the Spanish grant PGC2018-095317-B-C21 within the European Funds for Regional Development (FEDER).The recent increase in volume and complexity of available astronomical data has led to a wide use of supervised machine learning techniques. Active learning strategies have been proposed as an alternative to optimize the distribution of scarce labeling resources. However, due to the specific conditions in which labels can be acquired, fundamental assumptions, such as sample representativeness and labeling cost stability cannot be fulfilled. The Recommendation System for Spectroscopic followup (RESSPECT) project aims to enable the construction of optimized training samples for the Rubin Observatory Legacy Survey of Space and Time (LSST), taking into account a realistic description of the astronomical data environment. In this work, we test the robustness of active learning techniques in a realistic simulated astronomical data scenario. Our experiment takes into account the evolution of training and pool samples, different costs per object, and two different sources of budget. Results show that traditional active learning strategies significantly outperform random sampling. Nevertheless, more complex batch strategies are not able to significantly overcome simple uncertainty sampling techniques. Our findings illustrate three important points: 1) active learning strategies are a powerful tool to optimize the label-acquisition task in astronomy, 2) for upcoming large surveys like LSST, such techniques allow us to tailor the construction of the training sample for the first day of the survey, and 3) the peculiar data environment related to the detection of astronomical transients is a fertile ground that calls for the development of tailored machine learning algorithms.HPI Research Center in Machine Learning and Data Science at UC IrvineCNRS 2017 MOMENTUM grant under the project Active Learning for Large Scale Sky SurveysFCT under Project CRISP PTDC/FIS-AST-31546/2017Hewlett Packard Enterprise Data Science Institute (HPE DSI) at the University of HoustonGordon and Betty Moore Foundation postdoctoral fellowship at the University of California, Santa CruzSpace Telescope Science InstituteNational Aeronautics & Space Administration (NASA) HF2-51462.001 NAS5-26555International Gemini Observatory, a program of NSF's NOIRLabNational Science Foundation (NSF)Max Planck SocietyFoundation CELLEXAlexander von Humboldt FoundationEuropean Commission 839090Spanish grant within the European Funds for Regional Development (FEDER) PGC2018-095317-B-C2

Development of Mural Cells: From In Vivo Understanding to In Vitro Recapitulation

Mural cells are indispensable for the development and maintenance of healthy mature vasculature, valuable for vascular therapies and as developmental models. However, their functional plasticity, developmental diversity, and multitude of differentiation pathways complicate in vitro generation. Fortunately, there is a vast pool of untapped knowledge from in vivo studies that can guide in vitro engineering. This review highlights the in vivo genesis of mural cells from progenitor populations to recruitment pathways to maturation and identity with an emphasis on how this knowledge is applicable to in vitro models of stem cell differentiation

De Novo and Bi-allelic Pathogenic Variants in NARS1 Cause Neurodevelopmental Delay Due to Toxic Gain-of-Function and Partial Loss-of-Function Effects

Aminoacyl-tRNA synthetases (ARSs) are ubiquitous, ancient enzymes that charge amino acids to cognate tRNA molecules, the essential first step of protein translation. Here, we describe 32 individuals from 21 families, presenting with microcephaly, neurodevelopmental delay, seizures, peripheral neuropathy, and ataxia, with de novo heterozygous and bi-allelic mutations in asparaginyl-tRNA synthetase (NARS1). We demonstrate a reduction in NARS1 mRNA expression as well as in NARS1 enzyme levels and activity in both individual fibroblasts and induced neural progenitor cells (iNPCs). Molecular modeling of the recessive c.1633C>T (p.Arg545Cys) variant shows weaker spatial positioning and tRNA selectivity. We conclude that de novo and bi-allelic mutations in NARS1 are a significant cause of neurodevelopmental disease, where the mechanism for de novo variants could be toxic gain-of-function and for recessive variants, partial loss-of-function

De Novo and Bi-allelic Pathogenic Variants in NARS1 Cause Neurodevelopmental Delay Due to Toxic Gain-of-Function and Partial Loss-of-Function Effects.

Aminoacyl-tRNA synthetases (ARSs) are ubiquitous, ancient enzymes that charge amino acids to cognate tRNA molecules, the essential first step of protein translation. Here, we describe 32 individuals from 21 families, presenting with microcephaly, neurodevelopmental delay, seizures, peripheral neuropathy, and ataxia, with de novo heterozygous and bi-allelic mutations in asparaginyl-tRNA synthetase (NARS1). We demonstrate a reduction in NARS1 mRNA expression as well as in NARS1 enzyme levels and activity in both individual fibroblasts and induced neural progenitor cells (iNPCs). Molecular modeling of the recessive c.1633C>T (p.Arg545Cys) variant shows weaker spatial positioning and tRNA selectivity. We conclude that de novo and bi-allelic mutations in NARS1 are a significant cause of neurodevelopmental disease, where the mechanism for de novo variants could be toxic gain-of-function and for recessive variants, partial loss-of-function

Ultra-Rare Genetic Variation in the Epilepsies : A Whole-Exome Sequencing Study of 17,606 Individuals

Sequencing-based studies have identified novel risk genes associated with severe epilepsies and revealed an excess of rare deleterious variation in less-severe forms of epilepsy. To identify the shared and distinct ultra-rare genetic risk factors for different types of epilepsies, we performed a whole-exome sequencing (WES) analysis of 9,170 epilepsy-affected individuals and 8,436 controls of European ancestry. We focused on three phenotypic groups: severe developmental and epileptic encephalopathies (DEEs), genetic generalized epilepsy (GGE), and non-acquired focal epilepsy (NAFE). We observed that compared to controls, individuals with any type of epilepsy carried an excess of ultra-rare, deleterious variants in constrained genes and in genes previously associated with epilepsy; we saw the strongest enrichment in individuals with DEEs and the least strong in individuals with NAFE. Moreover, we found that inhibitory GABA(A) receptor genes were enriched for missense variants across all three classes of epilepsy, whereas no enrichment was seen in excitatory receptor genes. The larger gene groups for the GABAergic pathway or cation channels also showed a significant mutational burden in DEEs and GGE. Although no single gene surpassed exome-wide significance among individuals with GGE or NAFE, highly constrained genes and genes encoding ion channels were among the lead associations; such genes included CACNAIG, EEF1A2, and GABRG2 for GGE and LGI1, TRIM3, and GABRG2 for NAFE. Our study, the largest epilepsy WES study to date, confirms a convergence in the genetics of severe and less-severe epilepsies associated with ultra-rare coding variation, and it highlights a ubiquitous role for GABAergic inhibition in epilepsy etiology.Peer reviewe

A922 Sequential measurement of 1 hour creatinine clearance (1-CRCL) in critically ill patients at risk of acute kidney injury (AKI)

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